# A comparative study of Different Machine Learning Regressors For Stock Market Prediction

arXiv: https://arxiv.org/abs/2104.07469 · HTML: https://mlsanity.com/paper/2104.07469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Adaptive elastic net and Separate Selection from Least Squares for ultra-high dimensional regression models](https://mlsanity.com/paper/1410.3566/) (2014)

## Built on by (most cited first, 1 of 1)

- [Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models](https://mlsanity.com/paper/2205.12746/) (2022)

## Abstract (arXiv)

For the development of successful share trading strategies, forecasting the course of action of the stock market index is important. Effective prediction of closing stock prices could guarantee investors attractive benefits. Machine learning algorithms have the ability to process and forecast almost reliable closing prices for historical stock patterns. In this article, we intensively studied NASDAQ stock market and targeted to choose the portfolio of ten different companies belongs to different sectors. The objective is to compute opening price of next day stock using historical data. To fulfill this task nine different Machine Learning regressor applied on this data and evaluated using MSE and R2 as performance metric.